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ai-audio-datasets-list

This is a list of datasets consisting of speech, music, and sound effects, which can provide training data for Generative AI, AIGC, AI model training, intelligent audio tool development, and audio applications. It is mainly used for speech recognition, speech synthesis, singing voice synthesis, music information retrieval, music generation, etc.

aimuvi-1

ano_pred_cvpr2018

Official implementation of Paper Future Frame Prediction for Anomaly Detection -- A New Baseline, CVPR 2018

awesome-deep-learning-music

List of articles related to deep learning applied to music

awesome-large-audio-models

Collection of resources on the applications of Large Language Models (LLMs) in Audio AI.

chestx-ray-14

Segmentation Guided Thoracic Classification

chestx-ray14-cnn

CNN architectures, training procedures, and evaluations for prediction of diseases on the ChestX-ray14 dataset.

cnn_uim

Convolutional Neural Network (CNN) for modeling user interests

ct2016

Simulation data generator for complex biosensor DB

gdl_code

The official code repository for examples in the O'Reilly book 'Generative Deep Learning'

genmusic_demo_list

a list of demo websites for automatic music generation research

gnnpapers

Must-read papers on graph neural networks (GNN)

kjysmu.github.io

Homepage

knowledge-distillation-zoo

mask_rcnn

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

miccai_challenge_monuseg

This repo is for Miccai monuseg challenge, by Jimmy from aetherAI

mir-datasets

Simple collection of MIR datasets with metadata and links

muspy

A toolkit for symbolic music generation

mysite

test

newscrawler

News media articles crawler using python

nnaudio

Audio processing by using pytorch 1D convolution network

noisy2clean

Noisy2Clean: Novel Speech Denoising Framework using Diffusion Model

pyanomaly

Useful Toolbox for Anomaly Detection

sentizer

Sentiment analysis of tweets

tm-gist

Semeval17

uim

Modeling user interests using labeled dataset

wikiminer

User Interest modeling using Wikipedia and News Media